Managing airflow provisioning

ABSTRACT

In an implementation, a method for managing airflow provisioning in an area comprising a plurality of racks, wherein a plurality of fluid moving devices are to supply airflow to the plurality of racks through a plurality of adjustable vent tiles, includes accessing a model that describes airflow transport and distribution within the area, said model comprising a plurality of parameters, determining values for the plurality of parameters, and implementing the model to partition the area into a plurality of fluid moving device zones of influence with a desired level of overlapping among the plurality of fluid moving device zones of influence.

BACKGROUND

Data centers typically include multiple cooling units, such as, computer room air conditioning (CRAG) units, arranged to supply cooling airflow to a plurality of servers arranged in a rows of racks. The cooling airflow is often supplied through vent tiles distributed at multiple locations on a raised floor. More particularly, the fluid moving devices supply cooling airflow into a plenum formed beneath the raised floor and the cooling airflow is supplied to the servers through the vent tiles.

The cooling units are typically operated to substantially ensure that the temperatures in the servers are maintained within predetermined temperature ranges. That is, to largely prevent the servers from reaching temperature levels at which the servers operate inefficiently or are harmful to the servers, the cooling units are typically operated to supply cooling resources at lower temperatures and/or at higher volume flow rates than are necessary to maintain the servers within the predetermined temperature ranges. This over-provisioning of cooling resources is inefficient, increases operational costs of the data center, and shortens the life span of the cooling units.

BRIEF DESCRIPTION OF DRAWINGS

Features of the present disclosure are illustrated by way of example and not limited in the following figure(s), in which like numerals indicate like elements, in which:

FIG. 1 illustrates a simplified block diagram of a section of a data center, according to an example of the present disclosure;

FIG. 2 shows a block diagram of a system for managing airflow provisioning in the data center depicted in FIG. 1, according to an example of the present disclosure.

FIG. 3 illustrates a flow diagram of a method for managing airflow provisioning in the data center depicted in FIG. 1, according to an example of the present disclosure;

FIGS. 4 and 5, respectively, depict flow diagrams of methods of implementing the model disclosed herein in managing airflow provisioning depicted in FIG. 3, according to two examples of the present disclosure;

FIG. 6 depicts a control diagram that includes an MPC that implements the model disclosed herein, according to an example of the present disclosure; and

FIG. 7 illustrates a block diagram of a computing device to implement the methods depicted in FIGS. 3-5, according to example of the present disclosure.

DETAILED DESCRIPTION

For simplicity and illustrative purposes, the present disclosure is described by referring mainly to an example thereof. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be readily apparent however, that the present disclosure may be practiced without limitation to these specific details. In other instances, some methods and structures have not been described in detail so as not to unnecessarily obscure the present disclosure. As used herein, the term “includes” means includes but not limited to, the term “including” means including but not limited to. The term “based on” means based at least in part on. In addition, the variables “l”, “m”, and “n” are intended to denote integers equal to or greater than one and may denote different values with respect to each other.

Disclosed herein are a method and apparatus for managing airflow provisioning in an area, such as, a data center. More particularly, the airflow provisioning is managed through implementation of a model that describes airflow transport and distribution within the area. According to an example, the model comprises a physics based state-space model. In addition, parameters for the model are determined and the model is implemented in managing the airflow provisioning. The airflow provisioning includes the determination of the temperatures and volume flow rates of airflow supplied by a plurality of fluid moving devices as well as the volume flow rates of airflow supplied through a plurality of adjustable vent tiles.

The model disclosed herein is a holistic model, in that, zonal and local level actuations are coordinated. More particularly, the model disclosed herein captures the airflow resources provisioning, transport, and distribution, and incorporates both zonal airflow actuation, including the fluid moving device supply air temperature and blower speed, and local airflow provisioning actuation (for instance, from adaptive vent tiles). One result of this coordination is that fighting among various airflow actuations is substantially eliminated, while substantially optimal airflow provisioning efficiency is attained. In another regard, implementation of the model disclosed herein enables data centers to be partitioned into fluid moving device zones of influence with adjustable levels of overlapping among the fluid moving device zones of influence.

Implementation of the model disclosed herein also enables dynamic prediction of transient trajectories of the rack inlet temperatures based upon their current thermal statuses for any given zonal and local airflow actuations. In other words, the model disclosed herein may be implemented to dynamically predict how the rack inlet temperatures evolve over time. Implementation of the model disclosed herein further enables the determination of future rack inlet temperatures to be determined once current rack inlet temperatures and airflow actuations to be applied are given. In other words, future rack inlet temperatures may be determined without performing iterative equation solving. Moreover, the model disclosed herein enables all of the above features to be attained in a computationally efficient manner because, according to an example, the model is explicit and only involves relatively simple calculations.

In a further regard, implementation of the model disclosed herein enables real-time airflow actuation optimization at both the zonal and local levels, for instance, through minimization of a cost function. As such, the airflow optimization techniques disclosed herein are able to detect thermal anomalies or inefficient airflow statuses and are able to correct those issues in a timely manner. Moreover, through use of a properly defined cost function(s), the apparatus disclosed herein actively seeks the optimal settings for all the fluid moving devices and local airflow provisioning actuation mechanisms to satisfy the target thermal status, while minimizing the cost function(s) of interest.

With reference first to FIG. 1, there is shown a simplified perspective view of a section of an area 100, in this instance, a data center, in which a method and apparatus for managing airflow provisioning may be implemented, according to an example. The data center 100 is depicted as having a plurality of racks 102 a-102 n, a plurality of fluid moving devices 114 a-114 l (only fluid moving devices 114 a-114 b are depicted in FIG. 1), and a plurality of sensors 120 a-120 n. The racks 102 a-102 n are depicted as being positioned on a raised floor 110 and as housing electronic devices 116. The electronic devices 116 comprise, for instance, computers, servers, bladed servers, disk drives, displays, etc. As shown in FIG. 1, airflow, such as cool airflow, is delivered through adjustable vent tiles 118 a-118 m in the floor 110 to the racks 102 a-102 n. The fluid moving devices 114 a-114 b generally operate to supply airflow into a space 112 beneath the raised floor 110, and in certain instances to cool heated airflow (indicated by the arrows 124). The fluid moving devices 114 a-114 b may comprise, for instance, air conditioning (AC) units that have actuators for controlling the temperature and the volume flow rate of the cooled airflow supplied by the fluid moving devices 114-114 b. In other examples, the fluid moving devices 114 a-114 b comprise heaters having actuators to control the temperature and volume flow rate of heated airflow supplied by the fluid moving devices 114 a-114 b.

The adjustable vent tiles (AVTs) 118 a-118 m comprise manually and/or automatically adjustable vent tiles. In any regard, the AVTs 118 a-118 m may be adjusted to thereby vary the volume flow rate of airflow supplied through the AVTs 118 a-118 m. When the AVTs 118 a-118 m comprise automatically adjustable vent tiles, actuators (not shown) are provided to vary the operational settings of the AVTs 118 a-118 m. In addition, each of the AVTs 118 a-118 m may also include an interface through which the AVTs 118 a-118 m may receive instruction signals from a controller 130. The operational settings of the AVTs 118 a-118 m may include the opening levels of the AVTs 118 that may be used to vary the volume flow rate of the airflow and, in some instances, a speed level of local fans used to vary the flow rates of the airflow through the AVTs 118 a-118 m. The AVTs 118 a-118 m may have many different suitable configurations and are thus not to be limited to any particular type of adjustable vent tile.

In any regard, the airflow contained in the space 112 may include airflow supplied by more than one of the fluid moving devices 114 a-114 n, and in certain instances, airflow recirculated into the space 112 from above the floor 110. Thus, characteristics of the airflow, such as, temperature, pressure, humidity, flow rate, etc., delivered to various locations in the data center 100 may substantially be affected by the operations of multiples ones of the fluid moving devices 114 a-114 n. As such, conditions at various locations in the data center 100 may substantially be affected by the operations of more than one of the fluid moving devices 114 a-114 n.

The sensors 120 a-120 n may be networked, in a wired and/or wireless manner, with the controller 130 to convey detected condition information to the controller 130. The detected conditions may include, for instance, temperatures at the inlets of the racks 102 a-102 n, temperatures at the outlets of the adjustable vent tiles 118, etc. The detected conditions may, in addition or alternatively, include other environmental conditions, such as, pressure, humidity, airflow velocity, etc. In this regard, the sensors 120 a-120 n comprise any suitable types of sensors to detect the conditions.

As discussed in greater detail herein below, environmental condition information collected by the sensors 120 a-120 n is used to determine various parameters of a model that describes airflow transport and distribution within the data center 100. In one example, the model comprises a physics based state-space model. As also discussed in greater detail herein below, the model further describes effects of actuations on the fluid moving devices 114 a-114 n as well as the settings of the adjustable vent tiles 118 on the airflow transport and distribution within the data center 100. In this regard, the model disclosed herein is a holistic model. Moreover, the model is implemented to manage airflow provisioning in the data center 100.

In one example, values obtained through implementation of the model are used to partition the data center 100 into a plurality of fluid moving device 114 a-114 n zones of influence with varying levels of overlapping among the plurality of fluid moving device 114 a-114 n zones. In another example, the obtained values are used to simultaneously control the plurality of fluid moving devices 114 a-114 n and the adjustable vent tiles 118 to manage airflow provisioning in the data center 100. In a further example, the obtained values are used in the minimization of a cost function to simultaneously control the fluid moving devices 114 a-114 n and the adjustable vent tiles 118, substantially in real time.

It should be understood that the data center 100 may include additional elements and that some of the elements described herein may be removed and/or modified without departing from a scope of the data center 100. In addition, the data center 100 may comprise a data center that is in a fixed location, such as a building, and/or a data center that is in a movable structure, such as a shipping container or other relatively large movable structure. Moreover, although particular reference has been made in the description of the area 100 as comprising a data center, it should be understood that the area 100 may comprise other types of structures, such as, a conventional room in building, an entire building, etc.

Although the controller 130 is illustrated in FIG. 1 as comprising an element separate from the electronic devices 116, the controller 130 may comprise or be integrated with an electronic device 116 without departing from a scope of the data center 100 disclosed herein. In addition, or alternatively, the controller 130 may comprise a set of machine readable instructions to operate on a computing device, for instance, one of the electronic devices 116 or a different computing device. Moreover, although a single controller 130 has been depicted in FIG. 1, a plurality of controllers 130 may be implemented to respectively control individual ones or groups of fluid moving devices 114 a-114 b and, in further examples, individual ones or groups of AVTs 118 a-118 m.

Turning now to FIG. 2, there is shown a block diagram of a system 200 for managing airflow provisioning in an area 100, such as the data center depicted in FIG. 1, according to an example. It should be understood that the system 200 may include additional components and that some of the components described herein may be removed and/or modified without departing from the scope of the system 200. For instance, the system 200 may include any number of sensors 120 a-120 n, memories, processors, fluid moving devices 114 a-114 l, AVTs 118 a-118 m, as well as other components, which may be implemented in the operations of the system 200.

As shown, the system 200 includes the fluid moving devices 114 a-114 l, the AVTs 118 a-118 m, the sensors 120 a-120 n, the controller 130, a data store 220, a processor 230, and a network 240. The controller 130 is further depicted as including an input/output module 202, a data collection module 204, a model accessing module 206, a parameter determining module 208, a managing module 210, and an actuation module 212. According to an example, the controller 130 comprises machine readable instructions stored, for instance, in a volatile or non-volatile memory, such as DRAM, EEPROM, MRAM, flash memory, floppy disk, a CD-ROM, a DVD-ROM, or other optical or magnetic media, and the like. In this example, the modules 202-212 comprise modules of machine readable instructions stored in the memory, which are executable by the processor 230. According to another example, the controller 130 comprises a hardware device, such as, a circuit or multiple circuits arranged on a board. In this example, the modules 202-212 comprise circuit components or individual circuits, which the processor 230 is to control. According to a further example, the controller 130 comprises a combination of modules with machine readable instructions and hardware modules.

In any regard, the processor 230 receives detected condition information from the sensors 120 a-120 n over the network 240, which operates to couple the various components of the system 200. The network 240 generally represents a wired or wireless structure in the data center 100 for the transmission of data and/or signals between the various components of the system 200. In addition, the processor 230 stores the detected condition information received from the sensors 120 a-120 n in the data store 220, which may comprise any suitable memory upon which the processor 230 may store data and from which the processor 230 may retrieve data. The data store 220 may comprise DRAM, EEPROM, MRAM, flash memory, floppy disk, a CD-ROM, a DVD-ROM, or other optical or magnetic media, and the like. Although the data store 220 has been depicted as forming a separate component from the controller 130, it should be understood that the data store 220 may be integrated with the controller 130 without departing from a scope of the system 200.

According to an example, the controller 130 outputs the determined operational settings of the fluid moving devices 114 a-114 l and, in some instances, the AVTs 118 a-118 m, such as but not limited to volume flow rate set point(s), instructions pertaining to the determined volume flow rate set point(s), determined supply temperature set point(s), instructions pertaining to the determined supply temperature set point(s), determined operational settings and/or instructions pertaining to the determined operational settings through the input/output module 202. Thus, for instance, the determined volume flow rate set points, determined supply temperature set points, and the determined operational settings may be outputted to a display upon which the outputted information may be displayed, a printer upon which the outputted information may be printed, a network connection over which the outputted information may be conveyed to another computing device, a data storage device upon which the outputted information may be stored, etc. According to another example, the controller 130 communicates instruction signals over the network 240 to the fluid moving devices 114 a-114 l and/or the AVTs 118 a-118 m. In this example, the fluid moving devices 114 a-114 l may vary the volume flow rates and/or supply air temperatures of the fluid moving devices 114 a-114 l to reach the determined set points as instructed by the controller 130. According to another example, the operational settings of the AVTs 118 a-118 m are varied to cause the AVTs 118 a-118 m to have the operational settings as instructed by the controller 130.

Various manners in which the modules 202-212 of the controller 130 may operate are discussed with respect to the methods 300-500 depicted in FIGS. 3-5. It should be readily apparent that the methods 300-500 respectively depicted in FIGS. 3-5 represent generalized illustrations and that other elements may be added or existing elements may be removed, modified or rearranged without departing from the scopes of the methods 300-500.

With reference first to FIG. 3, there is shown a flow diagram of a method 300 for managing airflow provisioning in an area, such as, a data center 100, according to an example. At block 302, a model that describes airflow transport and distribution within the area is accessed, for instance, by the model accessing module 206. The model may be stored in the data store 220 and the model accessing module 206 may access the model from the data store 220. The model comprises a plurality of parameters and describes the effects of actuations on the plurality of fluid moving devices 114 a-114 l on the airflow transport and distribution within the area. More particularly, the model describes the effects of actuations on the plurality of fluid moving devices 114 a-114 l and the AVTs 118 a-118 m on the transport and distribution of airflow supplied into the electronic devices 116. In this regard, the model disclosed herein is a holistic and efficient model because the model takes as input both the zonal airflow provisioning actuation of the fluid moving devices 114 a-114 l and the local airflow provisioning actuation of the AVTs 118 a-118 m.

According to an example, the model is a state-space model based on energy and mass balance principles. In a non-limiting example, the model is a physics based state-space model. An example of the physics based state-space model is described by the following equation:

$\begin{matrix} {{\left. {{T\left( {k + 1} \right)} = {{T(k)} + {\left\{ {\sum\limits_{i = 1}^{N_{CRAC}}\; {g_{i} \cdot \left\lbrack {{{SAT}_{i}(k)} - {T(k)}} \right\rbrack \cdot {{VFD}_{i}(k)}}} \right\} \cdot \left\{ {\sum\limits_{j = 1}^{N_{tile}}\; {b_{j} \cdot {U_{j}(k)}}} \right\rbrack}}} \right\} + C},} & {{Eqn}\mspace{14mu} (1)} \end{matrix}$

in which T represents a rack inlet temperature, k and k+1 represent discrete time steps, SAT_(i) and VFD_(i) are a supply air temperature and a blower speed of the ith fluid moving device 114 a-114 l, U_(j) is the opening of the jth adjustable vent tile 118 a-118 m, N_(CRAC) and N_(tile) are the number of fluid moving devices 114 a-114 l and adjustable vent tiles 118 a-118 m, respectively, and wherein g_(i) and b_(j) are parameters that capture influences of each fluid moving device i and adjustable vent tile j, respectively, and C denotes a temperature change due to reasons such as recirculation and reversed flow.

At block 304, values for the parameters in the model are determined, for instance, by the parameter determining module 208. Generally speaking, the parameter determining module 208 determines the values for the parameters through an analysis of detected condition data received from the sensors 120 a-120 n. More particularly, the parameter determining module 208 determines values for the parameters g_(i), b_(j) and C in Eqn (1) through an optimization process, in which the parameter values that minimize the difference between the thermal status (rack inlet temperatures) predicted by the model using the parameters (g_(i), b_(j), and C) being evaluated and the detected conditions. The parameters (g_(i), b_(j), and C) that result in the least amount of difference between the thermal status (rack inlet temperatures) predicted by the model are selected as the values for the parameters (g_(i), b_(j), and C). This optimization process is repeated for each rack inlet temperature since each rack inlet temperature is characterized by a different set of parameters. Alternatively, the parameter determining module 208 may implement the parameter optimization process for a plurality of different rack inlet temperatures in parallel.

At block 306, the model is implemented in managing airflow provisioning in the data center 100, for instance, by the managing module 210. Various examples of manners in which the model is implemented at block 306 are described in greater detail with respect to FIGS. 4 and 5. More particularly, FIGS. 4 and 5, respectively, depict flow diagrams of methods 400 and 500 of implementing the model in managing airflow provisioning, according to two examples. As such, for instance, either or both of the methods 400 and 500 may be implemented at block 306 in FIG. 3.

With reference first to FIG. 4, at block 402, influence levels of the plurality of fluid moving devices 114 a-114 l on temperatures at inlets of a plurality of racks 102 a-102 n are determined, for instance, by the managing module 210. According to an example, the influences of each of the fluid moving devices 114 a-114 l on the temperatures at the inlets of the racks are captured by the parameter g, in Eqn (1). By way of example, in a data center 100 having 8 fluid moving devices 114 a-114 l, each detected rack inlet temperature will have 8 influence levels, each representing the influence level of one fluid moving device 114 a-114 l.

At block 404, for each of the rack inlet temperatures, ratios between each of the determined influence levels corresponding to a particular fluid moving device 114 a-114 l and a largest influence level of the determined influence levels for that particular rack inlet temperature are calculated, for instance, by the managing module 210. Thus, in the example above, a respective ratio between each of the 8 influence levels and the largest influence level for a particular rack inlet temperature are calculated. As such, one of the ratios will be 1 because one of the ratios will be between the largest influence level and itself and the remaining ratios will be less than 1. In addition, block 404 may be repeated for each of the rack inlet temperatures to determine the respective ratios of the influence levels corresponding to each of the fluid moving devices 114 a-114 l.

At block 406, the data center 100 is partitioned into a plurality of fluid moving device zones of influence, for instance, by the managing module 210. The partitioning of the data center 100 includes identifying which of the rack inlet temperatures belong to which of the fluid moving device 114 a-114 l zones of influence. In other words, the partitioning of the data center 100 indicates which of the fluid moving devices 114 a-114 l have more significant influences on which of the rack inlet temperatures, and hence are the fluid moving devices 114 a-114 l that are to respond to the thermal status variation of the corresponding rack inlet temperatures. According to an example, the partitioning is performed based upon the ratios determined at block 404. More particularly, for instance, a rack inlet temperature that has the largest influence level to a first fluid moving device 114 a is considered to be within the zone of influence of the first fluid moving device 114 a. In addition, the rack inlet temperature is also considered to be within the zone of influence of a second fluid moving device 114 b if the ratio between the influence level of the second fluid moving device 114 b and the influence level of the first fluid moving device 114 a exceeds an overlapping threshold. As such, a higher overlapping threshold causes a relatively smaller level of overlapping between the zones of influence to occur because the rack inlet temperatures are likely to fall within a fewer number of fluid moving device zones of influence. In addition, a lower overlapping threshold causes a relatively larger level of overlapping between the zones of influence to occur because the rack inlet temperatures are likely to fall within a larger number of fluid moving device zones of influence. In one regard, therefore, the level of overlapping between the fluid moving device 114 a-114 l zones of influence may substantially be changed by varying the overlapping threshold.

Through use of the ratios between the influence levels and the respective largest influence level for each of the rack inlet temperatures instead of an absolute influence threshold to determine the fluid moving device zones of influence, the possibility of orphaned rack inlet temperatures during the partitioning process (that is, those rack inlet temperatures that do not belong to any fluid moving device zone of influence), may substantially be reduced. In addition, by tuning the overlapping threshold from 1 to 0, the partitioned zones may have the desired level of overlapping, ranging from disjoint zones to 100% overlapping between any two zones. In comparison, prior techniques for partitioning data centers do not have this flexibility because the overlapping is dependent on the absolute influence threshold, which has a relatively narrow range. Moreover, disjoint zone partitioning of a data center, for example, is impossible with the prior techniques. This is because the absolute influence threshold has to be sufficiently low to avoid orphaned rack inlet temperatures, and this low threshold will inevitably result in considerable overlapping between neighboring zones. Furthermore, the zone partition approach disclosed herein may be used for input-output pairing, which may be crucial for the development of distributed data center cooling control systems. For centralized data center cooling control design, the partition approach disclosed herein may be used to trim weak connections between the system inputs and outputs, which leads to more efficient controller design. Moreover, the tuning feature disclosed herein may also be used to adjust the level of redundancy in a data center based on operational policies (e.g., by designating varying levels of redundancy according to service level agreements, etc.).

In one regard, the fluid moving device zones of influence may be implemented in determining which of the fluid moving devices 114 a-114 l is to be manipulated based upon, for instance, changing conditions in the data center 100.

Turning now to FIG. 5, at block 502, a cost function is accessed, for instance, by the managing module 210 from the data store 220. According to an example, the cost function comprises the total airflow provisioning power consumption and is defined with respect to the airflow provisioning actuations available in the data center 100. The available airflow provisioning actuations in the data center 100 comprise temperature and volume flow rate of airflow supplied by the fluid moving devices 114 a-114 l as well as the openings in the AVTs 118 a-118 m.

At block 504, zonal and local airflow provisioning actuation are coordinated through use of the model to minimize the cost function, for instance, by the managing module 210. More particularly, for instance, a model predictive controller (MPC), shown in FIG. 6, uses the model to optimize the zonal and local airflow provisioning by minimizing the cost function. According to an example, given the current thermal status (rack inlet temperatures), the MPC may implement the model to predict future rack inlet temperature trajectories when the trajectories of the airflow actuations (VFD and SAT of the fluid moving devices 114 a-114 l and openings of the AVTs 118 a-118 m) are given. The prediction of the future rack inlet temperature trajectories may be used to evaluate all of the possible zonal and local actuations implemented at discrete time steps with the updated current thermal status, and thus, thermal anomalies may be handled, and operating cost may constantly be minimized in response to varying conditions within the data center 100.

Additionally at block 504, the model is implemented to minimize the cost function while substantially maintaining temperature levels at the rack inlets within predetermined ranges. Through implementation of the model, which considers both the zonal and local airflow provisioning actuations for purposes of minimizing the cost function, fighting among the various zonal and local airflow provisioning actuations is substantially eliminated. The following equation describes an example in which the cost function is the total cooling power:

Σ_(i=1) ^(N) ^(CRAC) └VFD _(i) ³(k)_(R) _(VFD) +(−SAT_(i)(k))_(R) _(SAT) └,  Eqn (2):

in which the cooling power incurred by all of the fluid moving devices 114 a-114 l are summed up, and for fluid moving device i, the blower power (VFD) increases with the third power of blower speed (VFD_(i)) while the chiller power decreases linearly with the supply air temperature SAT_(i).

At block 506, the coordinated zonal and local airflow provisioning actuation that minimizes the cost function accessed at block 502 is outputted, for instance, by the input/output module 202. According to an example, the settings for the VFDs and supply air temperatures in the fluid moving devices 114 a-114 l and the openings of the AVTs 118 a-118 m that have been determined to minimize the cost function while meeting temperature requirements of the electronic devices 116 are outputted. In one example, the settings are outputted to a display, another computing device, a data storage, a printer, etc. In another example, the actuation module 212 outputs control signals to the fluid moving devices 114 a-114 l and/or the AVTs 118 a-118 m to cause the fluid moving devices 114 a-114 l and/or the AVTs 118 a-118 m to operate at the determined settings.

An example of a control diagram 600 that includes the MPC 602 that implements the model disclosed herein is depicted in FIG. 6. As shown therein, the MPC 602, which comprises the model and an optimization module (not shown), receives as inputs, a cost function that the optimization module runs to minimize by selecting the most appropriate airflow actuations, a threshold temperature ( T_(ref) ) as the constraint of the optimization that future rack inlet temperatures must stay below, and rack inlet temperatures ( T), for future rack inlet temperature prediction using the model. In other words, the MPC 602 seeks to determine the optimal zonal and local airflow provisioning settings in the data center 604 represented by the SAT, VFD, and AVT depicted in FIG. 6, in response to dynamic IT workload. The airflow resources provisioning, transport, and distribution are coordinated because they are considered simultaneously in the same framework to minimize the airflow provisioning power.

Some or all of the operations set forth in the methods 300-500 may be contained as utilities, programs, or subprograms, in any desired computer accessible medium. In addition, the methods 300-500 may be embodied by computer programs, which can exist in a variety of forms both active and inactive. For example, they may exist as software program(s) comprised of program instructions in source code, object code, executable code or other formats. Any of the above may be embodied on a computer readable storage medium.

Example computer readable storage media include conventional computer system RAM, ROM, EPROM, EEPROM, and magnetic or optical disks or tapes. Concrete examples of the foregoing include distribution of the programs on a CD ROM or via Internet download. It is therefore to be understood that any electronic device capable of executing the above-described functions may perform those functions enumerated above.

Turning now to FIG. 7, there is shown a block diagram of a computing device 700 to implement the methods depicted in FIGS. 3-5, in accordance with examples of the present disclosure. The device 700 includes a processor 702, such as a central processing unit; a display device 704, such as a monitor; a network interface 708, such as a Local Area Network LAN, a wireless 802.11x LAN, a 3G mobile WAN or a WiMax WAN; and a computer-readable medium 710. Each of these components is operatively coupled to a bus 712. For example, the bus 712 may be an EISA, a PCI, a USB, a FireWire, a NuBus, or a PDS.

The computer readable medium 710 may be any suitable non-transitory medium that participates in providing instructions to the processor 702 for execution. For example, the computer readable medium 710 may be non-volatile media, such as an optical or a magnetic disk; volatile media, such as memory; and transmission media, such as coaxial cables, copper wire, and fiber optics.

The computer-readable medium 710 may also store an operating system 714, such as Mac OS, MS Windows, Unix, or Linux; network applications 716; and an airflow provisioning management application 718. The operating system 714 may be multi-user, multiprocessing, multitasking, multithreading, real-time and the like. The operating system 714 may also perform basic tasks such as recognizing input from input devices, such as a keyboard or a keypad; sending output to the display 704; keeping track of files and directories on the computer readable medium 710; controlling peripheral devices, such as disk drives, printers, image capture device; and managing traffic on the bus 712. The network applications 716 include various components for establishing and maintaining network connections, such as machine readable instructions for implementing communication protocols including TCP/IP, HTTP, Ethernet, USB, and FireWire.

The airflow provisioning management application 718 provides various components for managing airflow provisioning in a data center 100, as described above. The management application 718 may thus comprise controller 130. The management application 718 also includes modules for accessing a model that describes airflow transport and distribution within the area, the model comprising a plurality of parameters, determining values for the plurality of parameters, and implementing the model in managing airflow provisioning in the data center. In certain examples, some or all of the processes performed by the application 718 may be integrated into the operating system 714. In certain examples, the processes may be at least partially implemented in digital electronic circuitry, or in computer hardware, machine readable instructions (including firmware and/or software), or in any combination thereof.

Although described specifically throughout the entirety of the instant disclosure, representative examples of the present disclosure have utility over a wide range of applications, and the above discussion is not intended and should not be construed to be limiting, but is offered as an illustrative discussion of aspects of the disclosure.

What has been described and illustrated herein is an example of the disclosure along with some of its variations. The terms, descriptions and figures used herein are set forth by way of illustration only and are not meant as limitations. Many variations are possible within the spirit and scope of the disclosure, which is intended to be defined by the following claims—and their equivalents—in which all terms are meant in their broadest reasonable sense unless otherwise indicated. 

What is claimed is:
 1. A method for managing airflow provisioning in an area comprising a plurality of racks, wherein a plurality of fluid moving devices are to supply airflow to the plurality of racks through a plurality of adjustable vent tiles, said method comprising: accessing a model that describes airflow transport and distribution within the area, said model comprising a plurality of parameters; determining, by a processor, values for the plurality of parameters; and implementing the model to partition the area into a plurality of fluid moving device zones of influence with a desired level of overlapping among the plurality of fluid moving device zones of influence.
 2. The method according to claim 1, wherein the model further describes effects of actuations on the plurality of fluid moving devices on the airflow transport and distribution within the area.
 3. The method according to claim 1, wherein the model further describes effects of actuations on the plurality of fluid moving devices and the adjustable vent tiles on the transport and distribution of airflow supplied into the plurality of racks.
 4. The method according to claim 3, wherein the model is described by the following equation: ${\left. {{T\left( {k + 1} \right)} = {{T(k)} + {\left\{ {\sum\limits_{i = 1}^{N_{CRAC}}\; {g_{i} \cdot \left\lbrack {{{SAT}_{i}(k)} - {T(k)}} \right\rbrack \cdot {{VFD}_{i}(k)}}} \right\} \cdot \left\{ {\sum\limits_{j = 1}^{N_{tile}}\; {b_{j} \cdot {U_{j}(k)}}} \right\rbrack}}} \right\} + C},$ wherein T represents a rack inlet temperature, k and k+1 represent discrete time steps, SAT_(i) and VFD_(i) are a supply air temperature and a blower speed of the ith fluid moving device, U_(j) is the opening of the jth adjustable vent tile, N_(CRAC) and N_(tile) are the number of fluid moving devices and adjustable vent tiles, respectively, and wherein g_(i) and b_(j) are the parameters that capture influences of each fluid moving device i and adjustable vent tile j, respectively, and C denotes a temperature change.
 5. The method according to claim 1, wherein implementing the model to partition the area into a plurality of fluid moving device zones of influence with a level of overlapping further comprises: determining influence levels of the plurality of fluid moving devices on a plurality of rack inlet temperatures; for each of the plurality of rack inlet temperatures, calculating ratios between each of the determined influence levels and a largest influence level of the determined influence levels for that rack inlet temperature; and partitioning the data center into the plurality of fluid moving device zones of influence based upon the calculated ratios.
 6. The method according to claim 5, further comprising: setting an overlapping threshold value for the ratios, wherein overlapping threshold value is to substantially control the level of overlapping among the plurality of fluid moving device zones of influence.
 7. The method according to claim 1, wherein implementing the model further comprises implementing the model to simultaneously control the plurality of fluid moving devices and adjustable vent tiles to manage airflow provisioning in the area.
 8. The method according to claim 7, wherein implementing the model further to simultaneously control the plurality of fluid moving devices and adjustable vent tiles further comprises: accessing a cost function; and determining a coordinated actuation of the plurality of fluid moving devices and adjustable vent tiles through use of the model to minimize the cost function while substantially maintaining rack inlet temperatures within predetermined ranges.
 9. An apparatus for managing airflow provisioning in an area comprising a plurality of fluid moving devices and a plurality of adjustable vent tiles, said apparatus comprising: a memory storing at least one module comprising machine readable instructions to: access a model that describes airflow transport and distribution within the area, said model comprising a plurality of parameters; determine values for the plurality of parameters; and implement the model to partition the area in to a plurality of fluid moving device zones of influence with a level of overlapping among the plurality of fluid moving device zones of influence; and a processor to implement the at least one module.
 10. The apparatus according to claim 9, wherein the at least one module further comprises machine readable instructions to: determine influence levels of the plurality of fluid moving devices on a plurality of rack inlet temperatures; for each of the rack inlet temperatures, calculate ratios between each of the determined influence levels and a largest influence level of the determined influence levels for that rack inlet temperature; and partition the data center into the plurality of fluid moving device zones of influence based upon the calculated ratios.
 11. The apparatus according to claim 9, wherein the at least one module further comprises machine readable instructions to: access a cost function; determine a coordinated actuation of the plurality of fluid moving devices and adjustable vent tiles through use of the model to minimize the cost function while substantially maintaining rack inlet temperatures within predetermined ranges; and output the determined coordinated actuation.
 12. The apparatus according to claim 19, wherein the model is described by the following equation: ${\left. {{T\left( {k + 1} \right)} = {{T(k)} + {\left\{ {\sum\limits_{i = 1}^{N_{CRAC}}\; {g_{i} \cdot \left\lbrack {{{SAT}_{i}(k)} - {T(k)}} \right\rbrack \cdot {{VFD}_{i}(k)}}} \right\} \cdot \left\{ {\sum\limits_{j = 1}^{N_{tile}}\; {b_{j} \cdot {U_{j}(k)}}} \right\rbrack}}} \right\} + C},$ wherein T represents a rack inlet temperature, k and k+1 represent discrete time steps, SAT_(i) and VFD_(i) are a supply air temperature and a blower speed of the ith fluid moving device, U_(j) is the opening of the jth adjustable vent tile, N_(CRAC) and N_(tile) are the number of fluid moving devices and adjustable vent tiles, respectively, and wherein g_(i) and b_(j) are the parameters that capture influences of each fluid moving device i and adjustable vent tile j, respectively, and C denotes a temperature change.
 13. A non-transitory computer readable storage medium on which is embedded at least one computer program, said at least one computer program implementing a method for managing airflow provisioning in an area comprising a plurality of fluid moving devices and a plurality of adjustable vent tiles, said at least one computer program comprising computer readable code to: access a model that describes airflow transport and distribution within the area, said model comprising a plurality of parameters; determine values for the plurality of parameters; and implement the model to simultaneously control the plurality of fluid moving devices and the plurality of adjustable vent tiles, said at least one computer program further comprising computer readable code to: access to a cost function; and determine a coordinated actuation of the plurality of fluid moving devices and adjustable vent tiles through use of the model to minimize the cost function while substantially maintaining rack inlet temperatures within predetermined ranges.
 14. The non-transitory computer readable storage medium according to claim 14, wherein the model is described by the following equation: ${\left. {{T\left( {k + 1} \right)} = {{T(k)} + {\left\{ {\sum\limits_{i = 1}^{N_{CRAC}}\; {g_{i} \cdot \left\lbrack {{{SAT}_{i}(k)} - {T(k)}} \right\rbrack \cdot {{VFD}_{i}(k)}}} \right\} \cdot \left\{ {\sum\limits_{j = 1}^{N_{tile}}\; {b_{j} \cdot {U_{j}(k)}}} \right\rbrack}}} \right\} + C},$ wherein T represents a rack inlet temperature, k and k+1 represent discrete time steps, SAT_(i) and VFD_(i) are a supply air temperature and a blower speed of the ith fluid moving device, U_(j) is the opening of the jth adjustable vent tile, N_(CRAC) and N_(tile) are the number of fluid moving devices and adjustable vent tiles, respectively, and wherein g_(i) and b_(j) are the parameters that capture influences of each fluid moving device i and adjustable vent tile j, respectively, and C denotes a temperature change.
 15. The non-transitory computer readable storage medium according to claim 14, said at least one computer program further comprising computer readable code to: determine influence levels of the plurality of fluid moving devices on a plurality of rack inlet temperatures; for each of the plurality of rack inlet temperatures, calculate ratios between each of the determined influence levels and a largest influence level of the determined influence levels for that rack inlet temperature; and partition the data center into the plurality of fluid moving device zones of influence with a desired level of overlapping among the plurality of fluid moving device zones of influence based upon the calculated ratios. 